DNA microarray Totally Explained

Everything about Dna Microarray totally explained

:For terminology, see glossary below A DNA microarray is a high-throughput technology used in molecular biology and in medicine. It consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, that each contain picomoles of a specific DNA sequence. These can be a short section of a gene or other DNA element that are used as probes to hybridize a cDNA or cRNA sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by fluorescence-based detection of fluorophore-labelled target to determine relative abundance of nucleic acid sequences in the target. In standard microarrays the probes are bound to a solid surface by covalent attachment to a chemical matrix (via epoxy-silane, amino-silane, lysine, polyacrylamide or others). The solid surface can either be glass or a silicon chip, in which case they're commonly known as gene chip or colloquially Affy Chip when an Affymetrix chip is used. Some microarray platforms, such as Illumina, use microscopic beads, instead of the large solid support (glass or treated silicon) used in traditional microarrays. DNA arrays are different from other types of microarray only in that they either measure DNA or use DNA as part of its detection system.
DNA microarrays can be used to measure changes in expression levels or to detect of SNPs (see Types of arrays section). There are not only different microarray applications, but also differences in the fabrication and workings of the microarrays, which as a result differ in accuracy, efficiency, and cost (see fabrication section). Furthermore, additional factors important to microarray experiments is the experimental design and analysis methods of the data (see Bioinformatics section).

Types of arrays

   Arrays of DNA can either be spatially arranged, as in the commonly known gene or genome chip, DNA chip, or gene array, or can be specific DNA sequences tagged or labelled such that they can be independently identified in solution. The traditional solid-phase array is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip. The affixed DNA segments are known as probes (although some sources will use different nomenclature such as reporters), thousands of which can be placed in known locations on a single DNA microarray. Microarray technology evolved from Southern blotting, whereby fragmented DNA is attached to a substrate and then probed with a known gene or fragment. DNA microarrays can be used to detect DNA (for example, in comparative genomic hybridization); it also permits detection of RNA (most commonly as cDNA after reverse transcription) that may or may not be translated into proteins. The process of measuring gene expression via cDNA is referred to as "expression analysis" or expression profiling.
   Since there can be tens of thousands of distinct probes on an array, each microarray experiment can potentially accomplish the equivalent number of genetic tests in parallel. Arrays have therefore dramatically accelerated many types of investigations. The use of a collection of distinct DNAs in arrays for expression profiling was first described in 1987, and the arrayed DNAs were used to identify genes whose expression is modulated by interferon. These early gene arrays were made by spotting cDNAs onto filter paper with a pin-spotting device. The use of miniaturized microarrays for gene expression profiling was first reported in 1995, and a complete eukaryotic genome (Saccharomyces cerevisiae) on a microarray was published in 1997.
   Applications of these arrays include:

Gene expression profiling

In an mRNA or gene expression profiling experiment the expression levels of thousands of genes are simultaneously monitored to study the effects of certain treatments, diseases, and developmental stages on gene expression. For example, microarray-based gene expression profiling can be used to identify genes whose expression is changed in response to pathogens or other organisms by comparing gene expression in infected to that in uninfected cells or tissues.

Comparative genomic hybridization

Assessing genome content in different cells or closely related organisms.

SNP detection arrays

Identifying single nucleotide polymorphism among alleles within or between populations.

Chromatin immunoprecipitation on Chip

DNA sequences bound to a particular protein can be isolated by imunoprecipitating that protein (ChIP), these fragments can be then hybridized to a microarray (such as a tiling array) allowing the determination of protein binding site occupancy throughout the genome. Example protein to imunoprecipitate are histone modifications (H3K27me3, H3K4me2, H3K9me3, etc), Polycomb-group protein (PRC2:Suz12, PRC1:YY1) and trithorax-group protein (Ash1) to study the epigenetic landscape or RNA Polymerase II to study the transcription lanscape.

Genotyping microarrays

DNA microarrays can also be used to scan the entire sequence of a genome to identify genetic variation at certain locations. SNP microarrays are a type of DNA microarray that are used to identify genetic variation in individuals and across populations. or electrochemistry on microelectrode arrays.
In spotted microarrays, the probes are oligonucleotides, cDNA or small fragments of PCR products that correspond to mRNAs. The probes are synthesized prior to deposition on the array surface and are then "spotted" onto glass. A common approach utilizes an array of fine pins or needles controlled by a robotic arm that's dipped into wells containing DNA probes and then depositing each probe at designated locations on the array surface. The resulting "grid" of probes represents the nucleic acid profiles of the prepared probes and is ready to receive complementary cDNA or cRNA "targets" derived from experimental or clinical samples. This technique is used by research scientists around the world to produce "in-house" printed microarrays from their own labs. These arrays may be easily customized for each experiment, because researchers can choose the probes and printing locations on the arrays, synthesize the probes in their own lab (or collaborating facility), and spot the arrays. They can then generate their own labeled samples for hybridization, hybridize the samples to the array, and finally scan the arrays with their own equipment. This provides a relatively low-cost microarray that's customized for each study, and avoids the costs of purchasing often more expensive commercial arrays that may represent vast numbers of genes that are not of interest to the investigator. Publications exist which indicate in-house spotted microarrays may not provide the same level of sensitivity compared to commercial oligonucleotide arrays, possibly owing to the small batch sizes and reduced printing efficiencies when compared to industrial manufactures of oligo arrays. Applied Microarrays offers a commercial array platform called the "CodeLink" system where 30-mer oligonucleotide probes (sequences of 30 nucleotides in length) are piezoelectrically deposited on an acrylamide matrix without any contact being made between the depositing equipment and the array surface itself. These arrays are comparable in quality to most manufactured arrays and generally superior to in-house printed arrays.
In oligonucleotide microarrays, the probes are short sequences designed to match parts of the sequence of known or predicted open reading frames. Although oligonucleotide probes are often used in "spotted" microarrays, the term "oligonucleotide array" most often refers to a specific technique of manufacturing. Oligonucleotide arrays are produced by printing short oligonucleotide sequences designed to represent a single gene or family of gene splice-variants by synthesizing this sequence directly onto the array surface instead of depositing intact sequences. Sequences may be longer (60-mer probes such as the Agilent design) or shorter (25-mer probes produced by Affymetrix) depending on the desired purpose; longer probes are more specific to individual target genes, shorter probes may be spotted in higher density across the array and are cheaper to manufacture. One technique used to produce oligonucleotide arrays include photolithographic synthesis (Agilent and Affymetrix) on a silica substrate where light and light-sensitive masking agents are used to "build" a sequence one nucleotide at a time across the entire array. Each applicable probe is selectively "unmasked" prior to bathing the array in a solution of a single nucleotide, then a masking reaction takes place and the next set of probes are unmasked in preparation for a different nucleotide exposure. After many repetitions, the sequences of every probe become fully constructed. More recently, Maskless Array Synthesis from NimbleGen Systems has combined flexibility with large numbers of probes.

Two-channel vs. one-channel detection

Two-color microarrays or two-channel microarrays are typically hybridized with cDNA prepared from two samples to be compared (for example diseased tissue versus healthy tissue) and that are labeled with two different fluorophores. Fluorescent dyes commonly used for cDNA labelling include Cy3, which has a fluorescence emission wavelength of 570 nm (corresponding to the green part of the light spectrum), and Cy5 with a fluorescence emission wavelength of 670 nm (corresponding to the red part of the light spectrum). The two Cy-labelled cDNA samples are mixed and hybridized to a single microarray that's then scanned in a microarray scanner to visualize fluorescence of the two fluorophores after excitation with a laser beam of a defined wavelength. Relative intensities of each fluorophore may then be used in ratio-based analysis to identify up-regulated and down-regulated genes.
Oligonucleotide microarrays often contain control probes designed to hybridize with RNA spike-ins. The degree of hybridization between the spike-ins and the control probes is used to normalize the hybridization measurements for the target probes. Although absolute levels of gene expression may be determined in the two-color array, the relative differences in expression among different spots within a sample and between samples is the preferred method of data analysis for the two-color system. Examples of providers for such microarrays includes Agilent with their Dual-Mode platform, Eppendorf with their DualChip platform for fluorescence labeling, and TeleChem International with Arrayit.
In single-channel microarrays or one-color microarrays, the arrays are designed to give estimations of the absolute levels of gene expression. Therefore the comparison of two conditions requires two separate single-dye hybridizations. As only a single dye is used, the data collected represent absolute values of gene expression. These may be compared to other genes within a sample or to reference "normalizing" probes used to calibrate data across the entire array and across multiple arrays. Three popular single-channel systems are the Affymetrix "Gene Chip", the Applied Microarrays "CodeLink" arrays, and the Eppendorf "DualChip & Silverquant". One strength of the single-dye system lies in the fact that an aberrant sample can't affect the raw data derived from other samples, because each array chip is exposed to only one sample (as opposed to a two-color system in which a single low-quality sample may drastically impinge on overall data precision even if the other sample was of high quality). Another benefit is that data are more easily compared to arrays from different experiments; the absolute values of gene expression may be compared between studies conducted months or years apart. A drawback to the one-color system is that, when compared to the two-color system, twice as many microarrays are needed to compare samples within an experiment.

Microarrays and bioinformatics

Experimental Design

Due to the biological complexity of gene expression, the considerations of experimental design that are discussed in the expression profiling article are of critical importance if statistically and biologically valid conclusions are to be drawn from the data.

There are three main elements to consider when designing a microarray experiment. First, replication of the biological samples is essential for drawing conclusions from the experiment. Second, technical replicates (two RNA samples obtained from each experimental unit) help to ensure precision and allow for testing differences within treatment groups. The technical replicates may be two independent RNA extractions or two aliquots of the same extraction. Third, spots of each cDNA clone or oligonucleotide are present at least as duplicates on the microarray slide, to provide a measure of technical precision in each hybridization. It is critical that information about the sample preparation and handling is discussed in order to help identify the independent units in the experiment as well as to avoid inflated estimates of significance

Standardization

The lack of standardization in arrays presents an interoperability problem in bioinformatics, which hinders the exchange of array data. Various grass-roots open-source projects are attempting to facilitate the exchange and analysis of data produced with non-proprietary chips.

The "Minimum Information About a Microarray Experiment" (MIAME) checklist helps define the level of detail that should exist and is being adopted by many journals as a requirement for the submission of papers incorporating microarray results. MIAME describes the minimum required information for complying experiments, but not its format. Thus, as of 2007, whilst many formats can support the MIAME requirements there's no format which permits verification of complete semantic compliance.

The "MicroArray Quality Control (MAQC) Project" is being conducted by the FDA to develop standards and quality control metrics which will eventually allow the use of MicroArray data in drug discovery, clinical practice and regulatory decision-making.

The MicroArray and Gene Expression Data (MGED) group is working on the standardization of the representation of gene expression data and relevant annotations.

Statistical analysis

The analysis of DNA microarrays poses a large number of statistical problems, including the normalization of the data. There are dozens of proposed normalization methods in the published literature; as in many other cases where authorities disagree, a sound conservative approach is to try a number of popular normalization methods and compare the conclusions reached: how sensitive are the main conclusions to the method chosen?
From a hypothesis-testing perspective, the large number of genes present on a single array means that the experimenter must take into account a multiple testing problem: even if the statistical P-value assigned to a given gene indicates that it's extremely unlikely that differential expression of this gene was due to random rather than treatment effects, the very high number of genes on an array makes it likely that differential expression of some genes represent false positives or false negatives. Statistical methods tailored to microarray analyses have recently become available that assess statistical power based on the variation present in the data and the number of experimental replicates, and can help minimize type I and type II errors in the analyses.
A basic difference between microarray data analysis and much traditional biomedical research is the dimensionality of the data. A large clinical study might collect 100 data items per patient for thousands of patients. A medium-size microarray study will obtain many thousands of numbers per sample for perhaps a hundred samples. Many analysis techniques treat each sample as a single point in a space with thousands of dimensions, then attempt by various techniques to reduce the dimensionality of the data to something humans can visualize.

Relation between probe and gene

The relation between a probe and the mRNA that it's expected to detect is problematic. On the one hand, some mRNAs may cross-hybridize probes in the array that are supposed to detect another mRNA. On the other hand, probes that are designed to detect the mRNA of a particular gene may be relying on genomic EST information that's incorrectly associated with that gene.

Public databases of microarray data

Microarray Experiment Sets	Sample Profiles	as of Date
8094	205148	March 11, 2008
12742	?	April 1, 2007
~100	~2500	Sept. 1, 2007
~31	2093	April 1, 2007
~45	555	April 1, 2007
3798	?	March 11, 2008
41	1741	November 15, 2006
~100	?	November 15, 2007
612	24513	March 3, 2008

For a directory of Microarray Databases, see:

See also the Microarray databases page in Wikipedia

Online microarray data-analysis programs and tools

Several Open Directory Project categories list online microarray data analysis programs and tools:

Cyber-T uses a Bayesian probabilistic framework for microarray data analysis

Bioconductor: open source and open development software project for the analysis and comprehension of genomic data

Genevestigator : Web-based database and analysis tool to study gene expression across large sets of tissues, developmental stages, drugs, stimuli, and genetic modifications.

GeneCAT (Gene Co-expression Analysis Toolbox): Web-based database of gene expression data and expression analysis tools for Arabidopsis thaliana and barley.

GEPAS

: Gene Expression Profile Analysis Suite developed in the Bioinformatics Department of the CIPF

in Spain.

DAVID bioinformatics: A free online bioinformatics resources provides functional interpretation of large lists of genes derived from genomic studies

caCORRECT

Chip Artifact Correction: A free online quality control tool for Affymetrix microarrays hosted at

Georgia Tech Biomedical Engineering Department

"spm"

is an open source package developed in R for the analysis of gene expression data by means of multivariate projection methodsFurther Information

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